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Biomedical Optics Express

Biomedical Optics Express

  • Editor: Joseph A. Izatt
  • Vol. 4, Iss. 4 — Apr. 1, 2013
  • pp: 514–519

Band selection in spectral imaging for non-invasive melanoma diagnosis

Ianisse Quinzán, José M. Sotoca, Pedro Latorre-Carmona, Filiberto Pla, Pedro García-Sevilla, and Enrique Boldó  »View Author Affiliations

Biomedical Optics Express, Vol. 4, Issue 4, pp. 514-519 (2013)

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A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.

© 2013 OSA

OCIS Codes
(110.3080) Imaging systems : Infrared imaging
(170.1870) Medical optics and biotechnology : Dermatology
(170.4580) Medical optics and biotechnology : Optical diagnostics for medicine
(330.6180) Vision, color, and visual optics : Spectral discrimination
(110.4234) Imaging systems : Multispectral and hyperspectral imaging

ToC Category:
Spectroscopic Diagnostics

Original Manuscript: January 16, 2013
Revised Manuscript: February 26, 2013
Manuscript Accepted: February 26, 2013
Published: March 4, 2013

Ianisse Quinzán, José M. Sotoca, Pedro Latorre-Carmona, Filiberto Pla, Pedro García-Sevilla, and Enrique Boldó, "Band selection in spectral imaging for non-invasive melanoma diagnosis," Biomed. Opt. Express 4, 514-519 (2013)

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  1. “Cancer: Melanoma of skin,” Eur. Cancer Obs. (2011) http://eu-cancer.iarc.fr/cancer-11-melanoma-of-skin.html,en .
  2. B. D‘Alessandro and A. P. Dhawan, “Multispectral Transillumination Imaging of Skin Lesions for Oxygenated and Deoxygenated Hemoglobin Measurement,” in Proceedings of IEEE EMBS (2010), pp. 6637–6640.
  3. S. Kumar, J. Ghosh, and M. Crawford, “Best-bases feature extraction algorithms for classification of hyperspectral data,” IEEE Trans. Geosci. Remote Sens.39, 1368–1379 (2001). [CrossRef]
  4. I. Diebele, I. Kuzmina, A. Lihachev, J. Kapostinsh, A. Derjabo, L. Valeine, and J. Spigulis, “Clinical evaluation of melanomas and common nevi by spectral imaging,” Biomed. Opt. Express3, 467–472 (2012). [CrossRef] [PubMed]
  5. S. V. Patwardhan, A. P. Dhawan, and P. A. Relue, “Wavelength Selection for Multi-Spectral Imaging of Skin Lesions Using Nevoscope,” in Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference (2003), pp. 327–328. [CrossRef]
  6. A. P. Dhawan, B. D‘Alessandro, S. Patwardhan, and N. Mullani, “Multispectral Optical Imaging of Skin-Lesions for Detection of Malignant Melanomas,” in Proceedings of IEEE EMBS (2009), pp. 5352–5255.
  7. B. D‘Alessandro and A. P. Dhawan, “Blood Oxygen Saturation Estimation in Transilluminated Images of Skin Lesions,” in Proceedings of the IEEE-EMBS on BHI (2012), pp. 729–732.
  8. M. Elbaum, A. W. Kopf, H. S. Rabinovitz, R. G. B. Langley, H. Kamino, M. C. Mihm, A. J. Sober, G. L. Peck, A. Bogdan, D. Gutkowicz-Krusin, M. Greenebaum, S. Keem, M. Oliviero, and S. Wang, “Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: A feasibility study,” J. Am. Acad. Dermatol.44, 207–218 (2001). [CrossRef] [PubMed]
  9. R. Marchesini, A. Bono, S. Tomatis, C. Bartoli, A. Colombo, M. Lualdi, and M. Carrara, “In vivo evaluation of melanoma thickness by multispectral imaging and an artificial neural network: A retrospective study on 250 cases of cutaneous melanoma,” Tumori93, 170–177 (2007). [PubMed]
  10. I. Quinzán, P. Latorre Carmona, P. García, E. Boldó, F. Pla, V. García, R. Lozoya, and G. Pérez de Lucía, “Non-Invasive Melanoma Diagnosis Using Multispectral Imaging,” in Proceedings of ICPRAM (2012), pp. 386–393.
  11. SCHNEIDER Industrial optics: OEM. In http://www.schneiderkreuznach.com .
  12. F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Trans. Med. Imaging16(2), 187–198 (1997). [CrossRef] [PubMed]
  13. J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: A survey,” IEEE Trans. Med. Imaging22(8), 986–1004 (2003). [CrossRef] [PubMed]
  14. I. Guyon and A. Elisseeff, “An introduction to variable and feature selection,” J. Mach. Learn. Res.3, 1157–1182 (2003).
  15. T. M. Cover and J. A. Thomas, Elements of Information Theory (John Wiley And Sons, 1991). [CrossRef]
  16. P. Pudil, F. J. Ferri, J. Novovicova, and J. Kittler, “Floating search methods for feature selection with nonmonotonic criterion functions,” Proc. of the 12th Int. Conf. on Pat. Rec. (1994), Vol. 2, pp. 279–283.
  17. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, “SMOTE: Synthetic minority over-sampling technique,” J. Artif. Intell. Res.16, 321–357 (2002).
  18. C. Cortes and V. Vapnik, “Support-vector network,” Mach. Learn.20, 273–297 (1995). [CrossRef]

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